Beyond MACs: Hardware Efficient Architecture Design for Vision Backbones

1. 1Experimentally evaluate the correlation between MAC count and execution time on various hardware platforms
2. 2Analyze the shortcomings of using MACs as a metric for efficiency in vision backbone networks
3. 3Investigate alternative metrics and methods for measuring efficiency, such as hardware-aware neural architecture search
4. 4Design and optimize vision backbone architectures with hardware efficiency in mind, considering factors such as memory access patterns and computation parallelism

Computer vision engineers and researchers can benefit from this study as it highlights the importance of considering hardware efficiency when designing vision backbones, and can inform their decisions on model architecture and optimization

💡 MACs are not a reliable metric for measuring efficiency in vision backbone networks, especially on edge devices

🚀 Move beyond MACs for efficient vision backbones! 🤖